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Learning Agile Dodgeball Behaviors for Humanoid Robots
Agility and rapid decision-making are vital for humanoid robots to safely and effectively operate in dynamic, unstructured environments. In human contexts—whether in crowded spaces, industrial settings, or collaborative environments—robots must be capable of reacting to fast, unpredictable changes in their surroundings. This includes not only planned navigation around static obstacles but also rapid responses to dynamic threats such as falling objects, sudden human movements, or unexpected collisions. Developing such reactive capabilities in legged robots remains a significant challenge due to the complexity of real-time perception, decision-making under uncertainty, and balance control.
Humanoid robots, with their human-like morphology, are uniquely positioned to navigate and interact with human-centered environments. However, achieving fast, dynamic responses—especially while maintaining postural stability—requires advanced control strategies that integrate perception, motion planning, and balance control within tight time constraints.
The task of dodging fast-moving objects, such as balls, provides an ideal testbed for studying these capabilities. It encapsulates several core challenges: rapid object detection and trajectory prediction, real-time motion planning, dynamic stability maintenance, and reactive behavior under uncertainty. Moreover, it presents a simplified yet rich framework to investigate more general collision avoidance strategies that could later be extended to complex real-world interactions.
In robotics, reactive motion planning for dynamic environments has been widely studied, but primarily in the context of wheeled robots or static obstacle fields. Classical approaches focus on precomputed motion plans or simple reactive strategies, often unsuitable for highly dynamic scenarios where split-second decisions are critical.
In the domain of legged robotics, maintaining balance while executing rapid, evasive maneuvers remains a challenging problem. Previous work on dynamic locomotion has addressed agile behaviors like running, jumping, or turning (e.g., Hutter et al., 2016; Kim et al., 2019), but these movements are often planned in advance rather than triggered reactively. More recent efforts have leveraged reinforcement learning (RL) to enable robots to adapt to dynamic environments, demonstrating success in tasks such as obstacle avoidance, perturbation recovery, and agile locomotion (Peng et al., 2017; Hwangbo et al., 2019). However, many of these approaches still struggle with real-time constraints and robustness in high-speed, unpredictable scenarios.
Perception-driven control in humanoids, particularly for tasks requiring fast reactions, has seen advances through sensor fusion, visual servoing, and predictive modeling. For example, integrating vision-based object tracking with dynamic motion planning has enabled robots to perform tasks like ball catching or blocking (Ishiguro et al., 2002; Behnke, 2004). Yet, dodging requires a fundamentally different approach: instead of converging toward an object (as in catching), the robot must predict and strategically avoid the object’s trajectory while maintaining balance—often in the presence of limited maneuvering time.
Dodgeball-inspired robotics research has been explored in limited contexts, primarily using wheeled robots or simplified agents in simulations. Few studies have addressed the challenges of high-speed evasion combined with the complexities of humanoid balance and multi-joint coordination. This project aims to bridge that gap by developing learning-based methods that enable humanoid robots to reactively avoid fast-approaching objects in real time, while preserving stability and agility.
**Work packages**
Literature research
Utilize simulation platforms (e.g., Isaac Lab) for initial policy development and training.
Explore model-free RL approaches, potentially incorporating curriculum learning to gradually increase task complexity.
Investigate perception models for object detection and trajectory forecasting, possibly leveraging lightweight deep learning architectures for real-time processing.
Implement and test learned behaviors on a physical humanoid robot, addressing the challenges of sim-to-real transfer through domain randomization or fine-tuning.
**Requirements**
Solid foundation in robotics, control theory, and machine learning.
Experience with reinforcement learning frameworks (e.g., PyTorch, TensorFlow, or RLlib).
Familiarity with robot simulation environments (e.g., MuJoCo, Gazebo) and real-world robot control.
Strong programming skills (Python, C++) and experience with sensor data processing.
**Publication**
This project will mostly focus on algorithm design and system integration. Promising results will be submitted to machine learning / robotics conferences.
**Work packages**
Literature research
Utilize simulation platforms (e.g., Isaac Lab) for initial policy development and training.
Investigate perception models for object detection and trajectory forecasting, possibly leveraging lightweight deep learning architectures for real-time processing.
Implement and test learned behaviors on a physical humanoid robot, addressing the challenges of sim-to-real transfer through domain randomization or fine-tuning.
**Requirements**
Solid foundation in robotics, control theory, and machine learning.
Experience with reinforcement learning frameworks (e.g., PyTorch, TensorFlow, or RLlib).
Familiarity with robot simulation environments (e.g., MuJoCo, Gazebo) and real-world robot control.
Strong programming skills (Python, C++) and experience with sensor data processing.
**Publication**
This project will mostly focus on algorithm design and system integration. Promising results will be submitted to machine learning / robotics conferences.
**Perception & Prediction**
- Develop a real-time perception pipeline capable of detecting and tracking incoming projectiles. Utilize camera data or external motion capture systems to predict ball trajectories accurately under varying speeds and angles.
**Reactive Motion Planning**
- Design algorithms that plan evasive maneuvers (e.g., side-steps, ducks, or rotational movements) within milliseconds of detecting an incoming threat, ensuring the robot’s center of mass remains stable throughout.
**Learning-Based Control**
- Apply reinforcement learning or imitation learning to optimize dodge behaviors, balancing between minimal energy expenditure and maximum evasive success. Investigate policy architectures that enable rapid reactions while handling noisy observations and sensor delays.
**Robustness & Evaluation**
- Test the system under diverse scenarios, including multi-ball environments and varying throw speeds. Evaluate the robot’s success rate, energy efficiency, and post-dodge recovery capabilities.
**Implementation on Humanoid Robot**:
- This is encouraged since we have our robot lying there waiting for you.
**Perception & Prediction**
- Develop a real-time perception pipeline capable of detecting and tracking incoming projectiles. Utilize camera data or external motion capture systems to predict ball trajectories accurately under varying speeds and angles.
**Reactive Motion Planning**
- Design algorithms that plan evasive maneuvers (e.g., side-steps, ducks, or rotational movements) within milliseconds of detecting an incoming threat, ensuring the robot’s center of mass remains stable throughout.
**Learning-Based Control**
- Apply reinforcement learning or imitation learning to optimize dodge behaviors, balancing between minimal energy expenditure and maximum evasive success. Investigate policy architectures that enable rapid reactions while handling noisy observations and sensor delays.
**Robustness & Evaluation**
- Test the system under diverse scenarios, including multi-ball environments and varying throw speeds. Evaluate the robot’s success rate, energy efficiency, and post-dodge recovery capabilities.
**Implementation on Humanoid Robot**:
- This is encouraged since we have our robot lying there waiting for you.
Please include your CV and transcript in the submission.
**Chenhao Li**
https://breadli428.github.io/
chenhli@ethz.ch
Please include your CV and transcript in the submission.
